ProtoProbes Tools for understanding sensory data in Makerspaces Written by P. Verburg
Coached by prof.dr.ir. L. M. G. Feijs Dated 7th of January 2016 Graduation Project Preparation (M2.1)
Introduction They are known under a confusing collection of names: Maker Labs, Design Factories, Make Labs, FabLabs, Makerspaces, Hackerspaces or TechShops. Nowadays more and more digital data is incorporated in the creations made in these spaces through sensors, actuators, signal processing, neural networks, etc. However, there is an important problem where the increase of complexity of these systems causes a bigger gap towards human interpretation making it difficult for designers, engineers or tinkerers to understand the technology they are working with. This makes the implementation of an envisioned design a tough job.
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Although the different types of these spaces are not alike, they all pursue the same ideal: providing a shared and collaborative environment with highend equipment where many different people can work on making things out of existing hardware, custom built electronics or handcrafted objects. Still, they lack interactive tools to support the understanding of digital data (Victor, 2014). This report shows the initial development of a tool to truly understand the interactive products designers, engineers and tinkerers are building in these spaces. It starts with the project definition, followed by the approach. Finally, the first and second design iterations are presented.
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table of contents 1. Project Definition
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1.1 Making as a human drive
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1.2 Makerspaces as context
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1.3 The maker movement
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1.4 Societal relevance
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1.5 Benchmarking
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2. Approach
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2.1 Three iterations
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2.2 Nine parts
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3. Iteration one
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3.1 Design space
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3.2 Prototype
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3.3 Findings
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3.4 Discussion
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4. Iteration two
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4.1 Expert meetings
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4.2 Workflows
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4.3 Design space
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4.4 Prototype
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4.5 Preliminary findings
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4.6 Discussion
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5. Reflection
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5.1 Passion and business
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5.2 Relativity and data
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5.3 Embodiment and web
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5.4 Aesthetics and interaction
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5.5 Protoprobes
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5.6 Vision on design & expertise
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5.7 Future
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6. Bibliography
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6.1 References
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6.2 Table of figures
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1. project Definition This chapter provides background information about the context, the target group and shows why this project is relevant for our society. This chapter can be skipped if the reader is familiar with the project and its official proposal.
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1.1 Making as a human drive Maker Labs, Design Factories, FabLabs, etc., are an embodiment of the drive of humans to make. It is the result of recent technological developments where a room is developed to provide the creators with the tools to create. We’ve been doing this for centuries in the form of workshops for carpenters, smiths, painters and many more creative people.
the physical world. However, seeing and understanding of digital information flowing through these components is still on a low level (with an oscilloscope or multimeter). The engineering of these flows becomes ad hoc and unreliable. For example, a process of trial and error is used to find a specific calibration value for a sensor or actuator.
Makerspaces spaces are a fairly new concept, still in development. Many people contribute to this new type of workshop. The concept ‘Seeing Spaces’ by Bret Victor is a visionary example. He noticed a current infrastructure in Makerspaces that mainly emphasizes the manipulation of physical materials and components through soldering, welding, 3D printing, sewing, sawing, laser cutting, etc (Victor, 2014). This is a logical choice, because all our previous types of workshops were about altering
These findings by Bret Victor have a strong relation with the vision of the Department of Industrial Design at the University of Technology in Eindhoven about embodying data. It led to the question how to realise a system to support the understanding of data in the maker movement with current technologies. How can Makers be empowered to truly understand the digital systems they are making?
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visual 1.1.1 - Seeing Spaces By Bret Victor (2014) 9
1.2 Makerspaces as Context During the past 10 years more and more collaborative spaces for making and hacking started to appear. A recent article by Cavalcanti (2013) shows they gained a lot of (still growing) popularity and that they are known by a variety of names. To prevent mixing up the names of these spaces 3 types are defined: Hackerspaces, Makerspaces and Makerspace Franchises. According to Calvalcanti Hackerspaces mainly facilitate the alterations of existing products. Makerspaces have a focus on the creation of new products through proof of concept prototypes.
Makerspace Franchises are more organized as they are part of a bigger network around the world and often ask membership fees (Calvalcanti, 2013). Visual 1.2.1 through 1.2.3 show an impression of the differences. This project focuses on Makerspaces, because they are more commonly found at (educational) institutions.
visual 1.2.1 - Hackerspaces have a focus on hacking existing products and facilitating workshops to do so.
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visual 1.2.2 - Makerspaces are more focused on facilitating crafting of physical shapes and testing interactions.
visual 1.2.3 - Makerspace Franchises have a professional touch to them whereas fees are common.
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1.3 The Maker movement as target group The Maker Movement is a broad definition of people, because in fact, it consists of all kinds of people. They are well described by Dougherty (2012), founder of Make Magazine. They are people who feel the need of empowering themselves to make stuff on their own. They attempt to cross the boundaries of the mass-produced products available (Dougherty, 2012). In the book ‘The Maker Movement Manifesto’ by Hatch (2013), cofounder of the Makerspace Franchise TechShop,
interesting!
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properties are summarized of a Maker: they make, share, give, learn, tool up, play, participate, support and change (Hatch, 2013). This description gives a clear impression of what kind of people are working with digital information. They are willing to use external tools (Hatch, 2013) and are in need of a tool that enables them to understand the digital information they are working with (Victor, 2014). Only a tool makes sure their creativity can be used to its full potential.
“Creating an embodiment of our digital world within the context of Maker Spaces for people who want to make stuff themselves.�
amazing!
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1.4 societal relevance Societal influences The development of Makerspaces is crucial to our modern times as it strives for shared technology for the middleclass of society. The non-profit association Educause (2013) published an article about the societal implications of Makerspaces. People not only use these spaces for the high-end equipment but also to share ideas and knowledge, to challenge each other and to overcome problems. The informal atmosphere is something people appreciate and it gives spontaneous ideas and innovation a chance. What Educause finds most important is that it enables people to learn on their own and switch quickly between disciplines. It is a wonderful environment for the creation of a self-directed learner (Educause, 2013). The notion of the self-directed learner is one of the current buzzwords within education. The development of these type of people is valuable as it is gaining popularity in this technology-driven society (Fischer & Sugimoto, 2006). This project aims to create a powerful tool in training self-directed learners at educational institutions to make complex
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products, systems and related services. They would be able to gain in-depth understanding of what they are creating. Future Other companies and educational institutions are also gaining awareness of the advantages and societal influences of these collaborative spaces in relation to innovation. For example, Loertscher (2012) illustrates how libraries are a context where this shift is starting to happen. Libraries will always be a center of knowledge, a place where one can integrate and synthesize facts and thoughts. In the 1960’s libraries were starting to use multimedia equipment, such as audio and video recordings. Now, during the last years, a transition is starting towards equipment that facilitates innovation and creativity with a basis in making. There is a fair chance many institutions are implementing environments like Makerspaces into their standard facilities (Loertscher, 2012).
1.5 Benchmarking Many of the current tools that embody data require to integrate a new system. Innovations can be found in the field of robotics. The latest tools allow seeing links between the real world and the perception of the robot (Annable et al., 2015; Gumbley & MacDonald, 2012).
They all rely on additional efforts to integrate the system, making it unattractive to use. Some need custom software packages, others need a specific development platform. This project would distinguish itself when finding a solution that has a low implementation threshold and is platform independent.
When looking at the maker movement there is not that much going on. Tools (visual 1.5.1 to 1.5.4) consist of solutions in Visual Studio (Visual Micro, 2012) or custom built software (Hobye, 2012; Fens, 2012; Plotly, 2015; Involt, 2016).
visual 1.5.1 - GUINO (Hobye, 2012)
visual 1.5.3 - Plot.ly (Plotly, 2015)
visual 1.5.2 - Visual Studio (Visual Micro, 2012)
visual 1.5.4 - Arduino Monitor (Fens, 2012) 15
2. Approach This project is split up into three iterations that all have one shared goal: creating a tool for Makerspaces to help people understand the digital information of interactive products in an easy way. The process through these iterations is of a converging nature and is driven by prototypes to reflect and iterate on. Furthermore, a total of nine parts are defined to create an overview of where important decisions need to be made; shaping the design space of this project. All of these parts are briefly explained in this chapter. Please note that they are not pillars on their own, they are connected with each other. This approach provides an effective mechanism to define a design space and to guide and communicate requirements for this specific project.
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2.1 Three Iterations Iteration one The first iteration is explorative. It tries to determine what options there are in creating a tool for understanding data in Makerspaces. A prototype will be built to make first steps towards a versatile technological infrastructure that can be used in future iterations.
Three iterations
Iteration two The second iteration focuses on how tools are integrated into the workflow in Makerspaces. Findings are implemented into a second prototype and validated at the Department of Industrial Design or externallly. Iteration three External validation is central in this final iteration. The previous two iterations provided the project with sufficient technological infrastructure to implement concrete findings from all iterations. Iteration three will be the moment of synthesis, integration and the answers on the main questions.
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Nine parts
2.2 Nine parts User evaluation This states in which context the iteration is explored and/or verified. Furthermore, it explains how this process will take place and how user input contributes to a future iteration. Medium The medium tells something about how the data is embodied. For example: is it simply a screen or does it have more extensive physical traits? Translation This part explains how the link between the real-world situation and the data is embodied. Beauty Defines in what ways the system comes across as aesthetically pleasing, as a trustworthy representation and as something to present your findings with.
Parameters States what the most important parameters are for the user to control and explicitly gain insights in. Interaction The interaction explains how the user can have influence on the data and parameters. Data behaviour This defines what key elements are of how the data changes over time. Data processing This part provides requirements about the data being processed, and if the user should have influence on this processing. Connectivity The connectivity part mentions how the data is transferred on a technical level.
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3. iteration one There are many ways to provide people with insights about the digital information they are working with. Victor (2014) shows in his concept of Seeing Spaces how makers should be able to see what their creation is seeing, to see how their creation is behaving, to compare different scenarios. One of the most common problems designers, engineers and tinkerers face during the development of a prototype is the intangibility of sensory data. To contain the scope of iteration one this specific direction is chosen.
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3.1 Design Space Visual 3.1.1 shows the possibilities for each of the parts from chapter 2.2. The creation of the prototype was done in parallel with the definition of the design space. The highlighted options show the
used implementation for the prototype. The choices are based on the vision of Victor (2014) with his concept Seeing Spaces.
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visual 3.1.1 - design space for iteration one
visual 3.2.1 - iteration one prototype with projector and data visual
3.2 Prototype The prototype is a small installation that can be expanded to fit the needs of a Makerspace. A beamer projects a real-time data visualization on your working surface next to your prototype (see visual 3.2.1). A custom-built web application can plot multiple graphs with multiple series of sensory data. Also, the application is able to perform instant data transformations; where for now only differentiation is implemented.
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User Interactions The interactions are limited for this version: one can connect with the appropriate serial port, can freeze the visualization and can toggle to a graph showing the mathematical differential. Technical Specifications The prototype consists of an ESP8266 microcontroller with Wi-Fi on board
events (user)
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(Espressif Systems, 2013) and a Chrome App (Google, 2016). Chrome Apps can directly connect with the serial ports allowing everything to be handled by the browser. In visual 3.3.2 one can see a flowchart depicting how the data is handled by the system.
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visual 3.2.2 - flow of sensory data through Chrome App 24
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3.3 Findings
3.4 Discussion
The first iteration has shown that web technologies can achieve real-time data transfers of at least a 50Hz rate. This allows every individual with access to modern web-browsers to be able to use the system.
Future iterations can make use of the technical infrastructure built in this initial version; it is made to be versatile to explore alternative visualizations and scale to multiple data streams.
The first responses of students (Masters Program, Industrial Design) are positive. They acknowledge the need to understand sensory data in Makerspaces. However, it remains difficult to discover what they need to truly understand their sensors and translate this to an implementation that satisfies their design requirements. By talking to the students it became apparent that experts, staff-members or alumni are the people for answering these questions.
Conclusions for the second iteration are: (1) Focus on an embodiment of the tool regarding the workflow makers have. (2) Don’t use projection for the data visualisation without a proper user interaction possibility. (3) Talk with experts about the understanding of data. (4) Focus on sensory data.
Furthermore, in conversation with prof. dr.ir. Martens it became clear a lot has been done in the field of data visualisation. What might be more important is how such a tool is used within the workflow of a Makerspace. Finally, the projection gives freedom to the placement of the visual, but when looking at the interaction with the visual it doesn’t make any sense for people. The projection comes across as clumsy and unnecessary.
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4. Iteration two The analysis of workflow processes is central in the second iteration. The goal is to create a prototype that is fully functional and is able to be efficiently integrated into the workflow of the target group (see chapter 1.3).
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4.1 Expert meetings A total of three experts have been consulted regarding the focus of this project. This chapter summarizes the questions that were asked and the most important findings.
Frank Delbressine Mechanical engineer
Panos Markopoulos user research expert
geert van den boomen electronics expert
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Delbressine has a lot of experience within the field of Mechanical Engineering and system design/thinking. When discussing the digital signal processing, it became apparent chaining of these data manipulations can give people insight in how different manipulations work together (for example a differential and auto correlation).
As the focus of sensory data is still broad, he urged to stick with simple sensors at the beginning. Answering what really creates the understanding of the sensors is more important. In this case understanding is about human interpretation. How to facilitate in finding the right interpretation?
Panos Markopoulos is an expert on user research and was asked how to measure integration into workflow. It became clear this type of research is worthwhile when done qualitatively. It is crucial to observe how people attach to such a tool. How attractive should it look? What makes them use it again? What makes them teach it to other people?
Geert van den Boomen is an expert in the field of electrical engineering. He recognizes the challenges for this project and is enthusiastic to install it at the Electronics Makerspace of the Department of Industrial Design. He stresses that some sensors are hard to measure, because they are digital or use a protocol. Still, making something that
visualizes every signal is unnecessary. What he encounters often is the lack of reproduction of measurements. There is no platform allowing makers to record and share data efficiently with, for example, external experts. How to share sensory data quickly and efficiently? How to ‘tag’ moments in the data set that are important?
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4.2 Workflows
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Each project and each person has their own unique workflow. This makes it difficult to describe a standard model for the context of a Makerspace. Due to the lack of literature on this subject
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1. problem definition
2. background research
Problem is of a societal nature.
Research societal context and create representation of the people in question
Problem is a technological challenge
Research related technologies
Problem is unclear or an experiment
Research related technologies and similair experiments
a framework will be presented in this chapter. Generally, a project in a Makerspace starts with (1) a societal problem, (2) a technological problem or (3) an experiment where the problem is
3. specify requirements
Determine in context of people
Determine in terms of functionality
Determine from performance expectations Perform initial tests
4. generate solutions Focus on societal impact and conceptual qualities Make mock-up prototypes Focus on reliability, effectiveness and feasability Make initial drawings and schematics
Focus on feasability Make components for the first ideas
not clear. These starting points define the decision making throughout the rest of the process. The below framework shows an iterative process with eight straightforward phases. Phases are
5. prototype solution
Collect required components Build breadboard prototype
Collect and make schematics and drawings Build proof of concept
Build experimental setup
6. test solution
highlighted red where integration of a tool for understanding of sensory data can be of use. One can see that the initial moment of use varies per type.
7. reflect on results
8. communicate results
Test in terms of influence on users
Reflect on changes in societal context
Focus on conceptual qualtities and societal innovation
Test in terms of functionalities and performance
Reflect on performance and optimizations
Focus on performance and technological innovation
Test in terms of expectations
Reflect on alternatives
Focus on performance and improvements
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4.3 design space In visual 4.3.1 one can see the new Design Space for iteration two. The options that will be implemented into the second prototype are highlighted and substantiated in this chapter. Medium A simple LCD screen is used, because the projection didn’t work well together with user interactions (see chapter 3.3). Also, an LCD screen allows possibilities to share data with external people (chapter 4.1). User Evaluation Two institutes have shown interest in testing the second iteration of ProtoProbes (chapter 3.3). Parameters Analogue signals are based on voltage over time to show the behavioural patterns. Beauty Victor (2013) puts forward the need to showing animated relations between data points when changing views. Furthermore, the prototype should communicate a look inviting people to grab and use it (chapter 4.1).
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Translation Data points are plotted into twodimensional graphs for comparison with existing solutions (chapter 1.5). Interaction A standard mouse and keyboard are chosen, because interaction possibilities are not the focus. The workflows (chapter 4.2) show it varies when and where a tool is needed. Modularity could be a solution to adjust the tool in such a way it is suitable for a variety of situations. Data Processing One should be able to chain mathematical manipulations to for example see the differential of a moving average (chapter 4.1). Data Behaviour Victor (2013) has shown different views on the same data set can create new human interpretations and understandings. Instantly changing between these views is central for the behaviour of the data. Connectivity What if the tool has its own wireless data transfer capabilities and is not dependent on other devices? This makes a big difference in comparison with existing solutions (chapter 1.5).
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visual 4.3.1 - design space for iteration two
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4.4 Prototype This prototype is a first fully functional version with (1) a data collection unit (see visual 4.4.1), (2) a data receiver unit (see visual 4.4.2) and (3) a visualisation unit (a computer). The complete system will now be referred to as ‘ProtoProbes’. The data collection unit (referred to as ‘probe’) is the most important component for the workflow. The remaining two are supposed to be integrated into the Makerspace on installation or to be used in combination with the computer of the maker.
visual 4.4.1 - reads analog data and sends it to the data receiver unit using Wi-Fi
User Interactions The probe is based on a cube where each side has a specific functionality. Visual 4.4.3 shows the mapping of the sides and its functionalities: (1) The indicator plane contains a NeoPixel LED to show the status (2) The connector plane is modular and allows different connectors depending on the investigated prototype. (3) The grip plane are two sides with each a Bezier to provide a comfortable grip when moving the probe around. (4) The attachment plane is modular whereas materials, such as, Velcro and clamps can slide into the slot. (5) The power plane is for charging the 500mAh battery and an on/off switch.
visual 4.4.2 - receives data and sends it to the visualisation unit via a serial connection
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1. indicator plane
2. connector plane
3. grip plane
4. attachment plane
visual 4.4.3 - division of functionalities for each plane of the shape
5. power plane 35
Technical Specifications (1) Both the probe and data receiver unit are based on the ESP8266 microcontroller which has built-in WiFi (Espressif Systems, 2016). The identical architectures makes it possible to use a probe as a data receiver unit as well. This means you only need one type of physical product. The software already allows switching between a sender and receiver mode, but it has not yet been implemented in this setup. The code can be found in appendix 1. The electronics schematic is shown in appendix 4. Visual 4.4.5 shows a flowchart of the software implementation.
visual 4.4.4 - the connectors are modular and can be put together by the maker himself 36
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visual 4.4.5 - boot sequence and thread of the data collector and data receiver unit; execution frequencies are noted when applicable
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Technical Specifications (2) Many existing visualisation applications are explored for the visualisation unit: HighCharts (HighCharts, 2016), Sketchify (Obrenovic & Martens, 2011), C3JS (Tanaka, 2016), Plot.ly (Plotly, 2016) and Rickshaw (Shutterstock, 2016). However, they all lack animated transition behaviours, are not optimized for real-time data or rely on additional software. For these reasons a bare-bones distribution of D3JS (D3JS, 2016) within a Chrome App (Google, 2016) was chosen. D3JS is a highly optimized visualisation framework allowing animated transitions and custom optimization. Visual 4.4.6 shows the result. In appendix 2 one can read the code for this Chrome App. Performance tests indicate a steady maximum CPU usage of 7% for a single real-time graph, whereas other frameworks would easily use 80%. Visual 4.4.7 to 4.4.10 show the unit in action.
visual 4.4.5 - initial tests are done with a simple LDR sensor 38
external input User
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visual 4.4.6 - flow of the sensory data through the Chrome App; execution frequencies are noted when applicable 39
visual 4.4.7 - raw data of LDR measurements
visual 4.4.8 - moving average manipulation added to the data set seen in visual 4.4.7
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visual 4.4.9 - differential manipulation added to the data set seen in visual 4.4.7
visual 4.4.10 - moving average and differential manipulation added to the data set seen in visual 4.4.7
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4.5 Preliminary Findings People were asked to try out the prototype at the end-term exhibition. A small setup was built with two sensors: a Light Dependent Resistor and a Flex Sensor. There were mixed responses. The junior students didn’t fully understand the advantages. They were not aware you sometimes need to capture behavioural patterns of sensor instead of static absolute values. Senior students and staff members acknowledged the problem and understood this would greatly help them and other people. The more experienced people know this implementation is comparable with a multi meter or oscilloscope. But somehow the ease of use of this prototype appeals to their way of working: it is portable, it connects instantly, it records data, it has sharing possibilities, it is modular and it invites to explore. Comments were mainly about the lack of functionalities within the visualisation or about the fact these small probes would get lost easily.
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For example, the ability to scroll through time, more manipulations and plotting multiple series in one graph were mentioned. Several staff members mentioned unexpected applications such as a 3D printer (for calibration purposes) and contextual research (for capturing users interacting with objects). Also, there is now contact with an employee of the MediaLab in Amsterdam (associated with the college of Amsterdam). She is interested in testing or buying a set of probes for their Makerspace. Conclusions ProtoProbes shows its potential to be integrated into the workflow of makers. The physical modularity of the connector and attachment modules appeals to makers, because it allows them to expand the use of the probes and customize it to their own workflow. Still, less experienced makers don’t immediately understand all the benefits such a tool offers.
4.6 Discussion What is currently lacking is an indepth user validation for the second iteration. According to the planning (see appendix 3) this should take place in the coming weeks. The next step is to give ProtoProbes to makers working on their own projects in a qualitative user test. Contextual inquiry (Schuler & Namioka, 1993) will be one of the methods for this user test, because it is perfectly suited for analyzing workflows. Schuler and Namioka explain this method as following: “Contextual inquiry is a technique that fosters participatory design. It provides a way for users to participate in the design of general purpose systems. It is a technique for working with users to help them articulate their current work practices, system practices and associated experiences.” This will hopefully answer the questions, such as, why the benefits of this tool are not clear for inexperienced makers?
limiting the application area. Digital signals and protocols are a common way of transferring sensory data as well. On a technological level these alternative applications are not difficult. The complexity starts with the integration into the workflow. The physical shape has been an important aspect of the integration. However, the form exploration has been on a low level. The new question that arises is: how to embody a wireless, modular and portable sensory data tranferring device? Furthermore, this prototype serves as a communication tool to external institutes. A visit is arranged to the Interactive Insitute in Umeå during the second semester. There are options to work together with the FabLab and university in Umeå as well. This prototype shows the possibilities and will hopefully trigger the interest of these institutes.
Alternative applications should be explored in the coming iteration. Currently there has been a focus on wearables and on-desk work. For example, implementations for 3D printers and contextual research have not been worked out. Also, the current prototype only supports analog signals,
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5. Reflection In this reflection I show how my other learning activities influenced the design process and what learning moments were important for this project and my competency development. What have I been up to the past semester? How did this shape the development within the expertise areas?
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5.1 Passion and Business I would like to start off with the Elective “Activating Your Innovation Radar�. At first I was sceptical about business tools (for example the Business Model Canvas). They came across as manipulative towards people. However, this elective has shown me that certain tools are suited for specific projects where each of them would try to capture the importance for society and people. The project we were working on had a bit of a personal load of the lecturers and clients; it was about getting older in our current society. I very much enjoyed the passion everybody had with this subject and it showed me that business is a tool to integrate this passion on a professional level and in context of the expertise area Business and Entrepreneurship. This is exactly what I now try to achieve with ProtoProbes.
visual 5.1.1 - clustering human needs for business purposes 46
5.2 Relativity and Data The Elective “Designing Information Products” has a strong connection with ProtoProbes and the expertise area Math, Data and Computing. It is about embodying different types of data visually. What I’ve come to realize is that absolute data is often not required to interpret it. Quite often, absolute data points don’t say anything and are arbitrary. What appears to be important is the behavioural pattern of data and how one presents this pattern.
visual 5.2.1 - design for showing relative data in context of s everal parameters of your immune system
This is something I have not yet integrated in ProtoProbes. It gives me a clear direction to look into showing behavioural patterns of data to support the understanding of it. 47
5.3 Embodiment and Web For the past two years I’ve been working on the administrative web-framework for associations called “Lassie”. Last semester it is installed at the study association of Industrial Design and Innovation Sciences. It keeps track of all the member information, bar transactions (Lucid. Bar), shop transactions (Lucid), e-lab transactions (E-Lucid), and much more. I’m currently one of the administrators and the main back-end developer. What it has shown me is a very practical thing for the expertise areas of User & Society and Technology & Realization: these systems need to evolve over time as processes of people change.
visual 5.3.1 - the member administration module of Lassie 48
For this reason the framework is very modular. But most importantly, since three weeks it allows external connections. A shift is happening where information from the administration system is embodied in physical devices revolving around the associations. For example, we want to check if someone has a coffee subscription when they place an order at the coffee machine. This made me realize a visualization tool such as ProtoProbes simply cannot be static. It should be open for adjustments to integrate new needs in understanding sensory data.
5.4 Aesthetics and Interaction This semester the concept of “Olly” from a previous Elective has been in the spotlights. It was shown on Mind The Step during the Dutch Design Week, it was featured on national television (RTLZ Nieuws) and shown on Bright. Olly is a new music experience. Music creates memories that nowadays get lost in the never ending offer of digital music. Olly invites you to re-experience the memories you have by randomly selecting songs from your past through looking at your Spotify account. For almost half a year two student and I have been engineering a fully standalone revised prototype. All this attention surprised us a bit. Because, in fact, the interactions of Olly are very poorly designed. However, the object looks so beautiful people are inclined to interact with it. It made me realize that within the expertise area of Creativity & Aesthetics beauty is so very important when it comes to embodying digital entities, such as ‘music from your past’ or a sensory data probe.
visual 5.4.1 - by turning the inner circle one can play the song Olly has for you; however, you don’t know wihch song 49
5.5 protoprobes So how will these four learning activities influence my graduation project? Well, (1) Activating Your Innovation Radar showed me the human side of Business & Entrepreneurship, (2) Designing Information Products taught me to use relativeness in data to the advantage of its interpretation, (3) Lassie made me realize there is a need of everchanging digital systems and (4) Olly made me aware of the nature of people where beautiful things simply appeal. During the first half of this graduation project I mainly learned to focus. Defining the design space (chapter 4.3) proved
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to be difficult for me and sometimes it helped to make a decision on instinct or a vision and opinion of someone else when literature is not available (for example Bret Victor or experts). By doing this I’ve became aware that this rather technological project is very much related to people and the expertise area User & Society. I’m confident the third iteration is going to be a major improvement for the concept, on both physical form giving and data embodiment.
5.6 vision on design & Expertise Another major question is what these learning moments imply for what kind of expert I want to be? My vision on design helps answering this question. Our society nowadays values the digital infrastructure and information highly, resulting in a constant effort of translating these intangible entities to things humans can understand. I believe that many products don’t use the enriching, intuitive and beautiful traits of the physical world to its highest potential. This creates a bigger gap towards human interpretation. A proper embodiment of this digital world requires a designer. More specifically: a designer who can define the right balance between what to embody and what to keep in the ‘digital black box’. This is exactly what I want to achieve with my projects: (1) Olly tries to capture the everlasting emotions associated with
music for digital music storage, (2) Lassie is shifting from a web-platform towards a central piece in an API network connected with physical devices, and (3) ProtoProbes is an artefact showing designers, engineers and tinkerers what amazing processes take place in (the most simple) sensors. This is where my passion lies and where I want to grow in expertise. I consider myself an expert in the field of webdevelopment, where I’ve been working in for the past 8 years. However, I don’t see the future in only web-development. Olly and ProtoProbes have shown me the importance of a physical manifestation of our digital world. Such an expertise might be described as: “a creative embodier of digital information and processes”. In this description mainly the expertise areas of Creativity & Aesthetics, Technology & Realization and Math, Data & Computing are integrated.
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5.7 Future I would like to conclude with several concrete goals for next semester to achieve such expertise. First of all I need to validate my embodiments in the outside world by (1) installing ProtoProbes in various Makerspaces and (2) exhibiting on at least one external exhibition. Other goals contribution to the first two are (3) creating smaller, more beautiful and more efficient probes (dedicated PCBs, shape exploration, other ways of modularity, support for other sensory signals), and finally, (4) making the visualisation framework opensource inviting people to explore new representations of their data (perhaps even physical).
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visual 5.7.1 - current collection of components for ProtoProbes 53
6. Bibliography
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6.1 References Annable, B., Budden, D. & Mendes, A. (2015). NUbugger: A Visual Real-Time Robot Debugging System. Cavalcanti , G. (2013). Is it a Hackerspace, Makerspace, TechShop, or FabLab? Retrieved from: http://makezine.com/2013/05/22/the-difference-between-hackerspacesmakerspaces-techshops-and-fablabs/. Visited on 13th of September 2015. Dougherty, D. (2012). The Maker Movement. Innovations: Technology, Governance, Globalization, 7(3), pp.11-14. Educause (2013). 7 Things You Should Know About‌ Makerspaces. Retrieved from: https://net.educause.edu/ir/library/pdf/eli7095.pdf. Visited on 13th of September 2015. Espressif Systems (2016). Esp8266. Retrieved from: http://espressif.com/en/products/ esp8266/. Visited on 4th of January 2016. Espressif Systems (2013). ESPRESSIF SMART CONNECTIVITY PLATFORM: ESP8266. D3JS (2016). D3JS - Data Driven Documents. Retrieved from: http://d3js.org/. Visited on 4th of January 2016. Fens, P. (2012). Arduino Monitor. Retrieved from: http://blog.pepf.nl/2012/08/arduinomonitor-an-easy-visualisation-tool-for-sensor-data/. Visited on 4th of January 2016. Fischer, G. & Sugimoto, M. (2006). Supporting Self-Directed Learners and Learning Communities with Sociotechnical Environments. Research and Practice in Technology Enhanced Learning, 01(01), pp.31-64. Google (2016). What Are Chrome Apps? Retrieved from: https://developer.chrome.com/apps/about_apps. Visited on 4th of January 2016.
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Gumbley, L. & MacDonald, B. A. (2012). Realtime Debugging for Robotics Software. Hatch, M. (2013). The maker movement manifesto. Hobye, M. (2012). GUINO Arduino GUI visualizer/debugger. Retrieved from: http://dangerousprototypes.com/2012/10/17/guino-arduino-gui-visualizerdebugger/. Visited on 13th of September 2015. Involt (2016). HTML to Arduino prototyping framework for designers. Retrieved from: http://involt.github.io/. Visited on 4th of January 2016. Kroski, E. (2013). A Librarian’s Guide to Makerspaces: 16 Resources. Retrieved from: http://oedb.org/ilibrarian/a-librarians-guide-to-makerspaces/. Visited on 13th of September 2015. Loertscher, D. V. (2012). Makerspaces and the Learning Commons. Teacher Librarian. 40.1 pp.45-46. Obrenovic, Z. & Martens, J.B. (2011). Sketching Interactive Systems with Sketchify. ACM Transactions on Computer Human Interaction (ToCHI), Vol. 18, no. 1. Plotly (2015). Arduino library for real-time logging and streaming data to online plotly graphs. Retrieved from: https://github.com/plotly/arduino-api. Visited on 4th of January 2016. Plotly (2016). The open source JavaScript graphing library that powers plotly. Retrieved from: https://plot.ly/javascript/. Visited on 4th of January 2016. Schuler, D. and Namioka, A. (1993). Participatory design. Hillsdale, N.J.: L. Erlbaum Associates.
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Shutterstock (2016). JavaScript toolkit for creating interactive real-time graphs. Retrieved from: http://code.shutterstock.com/rickshaw/. Visited on 4th of January 2016. Tanaka, M. (2016). A D3-based reusable chart library. Retrieved from: https://github.com/masayuki0812/c3. Visited on 4th of January 2016. Victor, B. (2014). Seeing Spaces. Retrieved from: http://www.worrydream.com/SeeingSpaces/. Visited on 13th of September 2015. Victor, B. (2013). Media For Thinking The Unthinkable. Retrieved from: http://worrydream.com/MediaForThinkingTheUnthinkable/. Visited on 13th of September 2015. Visual Micro (2012). Debug Arduino – Overview. Retrieved from: http://www.visualmicro. com/post/2012/05/05/Debug-Arduino-Overview.aspx. Visited on 13th of September 2015. Wensveen S. A. G. , Djajadiningrat J.P. , Overbeeke C.J. (2004). Interaction frogger: a design framework to couple action and function. Proceedings of the DIS’04, Cambridge, MA, USA, 1–4 August 2004
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6.2 Table of Figures Page 1, 2, 6 & 11: Makerspace https://www.flickr.com/photos/erasmushogeschool/albums/72157607369750642/ Page 9: Seeing Spaces http://www.worrydream.com/SeeingSpaces/ Page 10: Hackerspace https://www.flickr.com/photos/animakitty/9588952266 Page 11: FabLab https://www.flickr.com/photos/devonlibraries/15381278900/sizes/k/ Page 15: GUINO debugger http://dangerousprototypes.com/2012/10/17/guino-arduino-gui-visualizerdebugger/ Page 15: Visual Micro debugger http://playground.arduino.cc/Code/VisualMicro Page 15: Arduino Monitor http://blog.pepf.nl/2012/08/arduino-monitor-an-easy-visualisation-tool-for-sensordata/ Page 15: Plotly https://plot.ly/javascript/ Page 18: process icon process by Christopher Holm-Hansen from the Noun Project Page 49: Olly Wouter van der Wal (http://www.woutdesigner.com)
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Thank you for reading.